Systems and approaches for autotuning an injection molding machine

ABSTRACT

Systems and approaches for controlling an injection molding machine and a mold forming a mold cavity and being controlled according to an injection cycle. The systems and methods include analyzing a model of at least one of the injection molding machine, the mold, and a molten material to determine initial values for one or more control parameters of the injection molding machine, and executing a run of injection cycles at the injection molding machine; measuring operation of the injection molding machine during a particular injection cycle of the run of injection cycles; determining one or more operational parameters exceed a threshold; and upon determining that the one or more operational parameters exceed the threshold, adjusting the one or more control parameters for subsequent injection cycles of the run of injection cycles.

CROSS-REFERENCE TO RELATED APPLICATION

This non-provisional application is a continuation of U.S. applicationSer. No. 16/432,403, entitled “Systems and Approaches for Autotuning anInjection Molding Machine”, filed Jun. 5, 2019, which claims the benefitof the filing date of U.S. Provisional Application No. 62/692,265,entitled “Systems and Approaches for Autotuning an Injection MoldingMachine”, filed Jun. 29, 2018, the entirety of each of which is herebyincorporated by reference.

FIELD OF THE DISCLOSURE

The present disclosure relates generally to injection molding and, moreparticularly, to approaches for autotuning control parameters injectionmolding machines in response to varying operational parameters.

BACKGROUND

Injection molding is a technology commonly used for high-volumemanufacturing of parts constructed of thermoplastic materials. Duringrepetitive injection molding processes, a thermoplastic resin, typicallyin the form of small pellets or beads, is introduced into an injectionmolding machine which melts the pellets under heat and pressure. In aninjection cycle, the molten material is forcefully injected into a moldcavity having a particular desired cavity shape. The injected plastic isheld under pressure in the mold cavity and is subsequently cooled andremoved as a solidified part having a shape closely resembling thecavity shape of the mold. A single mold may have any number ofindividual cavities which can be connected to a flow channel by a gatethat directs the flow of the molten resin into the cavity. A typicalinjection molding process generally includes four basic operations: (1)heating the plastic in the injection molding machine to allow theplastic to flow under pressure; (2) injecting the melted plastic into amold cavity or cavities defined between two mold halves that have beenclosed; (3) allowing the plastic to cool and harden in the cavity orcavities while under pressure; and (4) opening the mold halves andejecting the part from the mold.

In these systems, a control system controls the injection moldingprocess according to an injection pattern that defines a series ofsetpoint values for control parameters of the various components of theinjection molding machine. For example, the injection cycle can bedriven by a fixed and/or a variable melt pressure profile whereby thecontroller uses sensed pressures at a nozzle as the input fordetermining a driving force applied to the material.

Changes in molding conditions can significantly affect properties of themolten plastic material. As an example, material specificationdifferences between resin batches and changes in environmentalconditions (such as changes in temperature or humidity) can raise orlower the viscosity of the molten plastic material. When viscosity ofthe molten plastic material changes, quality of the molded part may beimpacted. For example, if the viscosity of the molten plastic materialincreases, the molded part may be “under-packed” or less dense due to ahigher required pressure, after filling, to achieve optimal partquality. Conversely, if the viscosity of the molten plastic materialdecreases, the molded part may experience flashing as the thinner moltenplastic material is pressed into the seam of the mold cavity.Furthermore, recycled plastic material that is mixed with virginmaterial may impact the melt flow index (MFI) of the combined plasticmaterial. Inconsistent mixing of the two materials may also create MFIvariation between cycles.

Some conventional injection molding machines do not adjust the injectioncycle to account for these changes in material properties. As a result,these injection molding machines may produce lower quality parts, whichmust be removed during quality-control inspections, thereby leading tooperational inefficiencies. Moreover, as an injection molding run mayinclude hundreds, if not thousands, of injection cycles, theenvironmental conditions of the injection molding machine may not beconstant across each injection cycle of the run. Thus, even if theinjection cycle is adapted to account for the environmental factors atthe onset of the run, the changing environmental conditions may stillresult in the production of lower quality parts during injection cyclesexecuted later in the run.

Additionally, a reliance on an injection cycle based on a fixed meltpressure pattern, the injection cycle may not be capable of properlyinjecting materials having varying characteristics (e.g., regrind,biodegradable, and/or renewable materials). Additionally, while somesystems may use an adjustable melt pressure pattern, these systems areoftentimes incapable of maintaining material tolerances when materialspecifications (e.g., viscosity and part density) do change. As aresult, these systems may produce inconsistently-dimensioned parts, thusfurther increasing operational inefficiencies.

SUMMARY

Embodiments within the scope of the present invention are directed tothe control of injection molding machines to produce repeatablyconsistent parts by automatically retuning the control parameters of aninjection molding machine based on the operation of the injectionmolding machine. Systems and approaches for controlling the injectionmolding machine include first obtaining a model of the injection moldingmachine, a mold, and/or a material to determine an initial set ofcontrol parameters for the injection molding machine. For example, thecontrol parameters may include a melt pressure profile and/or gainvalues for a proportional-integral-derivative (PID) controller.Operation of the injection molding machine is measured during theinjection cycle. When operation is outside of an expected range ofoperation, the control parameters are automatically tuned (e.g.,adjusted based upon current operation of the injection molding machine).

As compared to conventional, fixed control of an injection moldingprocess across the various injection cycles of a run of injectioncycles, automatically tuning the control parameters can reduce thenumber of oscillations that occur and/or reduce the magnitude of theoscillations that do occur. Reducing the oscillations improves howclosely the performance of the injection molding machines matches thesetpoints defined by the injection cycle. Automatically tuning thecontrol parameters also causes the injection molding machine to achievesteady state values that more closely match those defined by theinjection cycle. As a result, the consistency at which the injectionmolding machines produces molded parts is improved.

In various embodiments, a controller of the injection molding machinemay be operatively connected to one or more sensors that monitor theoperating conditions of the injection molding machine. For example, onesensor may monitor a screw position; another sensor may monitor avelocity at which the screw rotates; still another sensor may monitor amold cavity pressure; and yet another sensor may monitor a temperatureof a thermoplastic material or of a heated barrel. The controller canobtain the sensor data generated by the one or more sensors to determinewhether or not the operation of the injection molding machine is withinthe expected range of operation.

In some embodiments, the controller compares a single parameter to athreshold value. For example, an overshoot pressure may exceed athreshold amount, an error in steady-state pressure may exceed athreshold amount, or a humidity in the injection molding machine'sambient environment may have shifted beyond a threshold amount. Inadditional or alternative embodiments, the controller may combine thesensor data to generate a composite metric or score that is compared toa threshold value. For example, the sensor data may be combined todetermine a metric indicative of the viscosity of the molten material.In some embodiments, one or more of the characteristics of the injectionmolding machine, mold, and/or the molten material indicated by theirrespective models are also used to determine the composite metric.

In some embodiments, one or more machine learning techniques are appliedto determine the composite metric and/or the threshold value to whichthe composite value is compared. For example, in some implementations,performance of a plurality of injection cycles is monitored for aplurality of different injection molding machines, molds, and moltenmaterials. Accordingly, this historical data can be used as an input totrain the machine learning algorithm to correlate the characteristics ofthe injection molding machine, mold, and/or molten material compiled intheir respective models and their impact on the measured response tobeing controlled in accordance with the injection cycle.

Therefore, the controller may determine the need to adjust the controlparameters of the injection molding process with more accuracy thanconventionally possible. Moreover, when compared to conventionalinjection molding systems that rely on manual monitoring of theinjection molding machine, the present techniques enable thedetermination of the need to adjust the control parameters based onrelationships beyond those capable of manual observation.

Further, different injection molding machines, molds, and/orthermoplastic materials may exhibit different performancecharacteristics when following the same injection pattern. For example,some injection molding machines may be used more frequently than otherinjection molding machines. Accordingly, moving parts in the injectionmolding machine may exhibit higher or lower resistivity depending on theparticular effects caused by wear and tear. As another example,different injection molding machines may be manufactured by differentcompanies using different processes. These differences may be quantifiedand represented by the model of the injection molding machine.

In some embodiments, the mold may also be modeled. The model of the moldmay include data associated with historic injection cycles executed byinjection molding machines. For example, the data may include anidentifier of the injection molding machine that executed the moldcycle, a plurality of injection pressure or injection velocity valuessensed over the course of the mold cycle, or other characteristics ofinjection molding machine when executing the mold cycle.

In some further embodiments, the molten material may also be modeled.The model of the molten material may include a MFI associated with thematerial and/or a correlation between molten material MFI and the ratioof regrind to unused molten material.

In various embodiments, the controller is also operatively connected toa model database that stores the models representative of injectionmolding machines, molds, and/or molten materials. The controller canobtain the models corresponding to the relevant injection moldingmachine, mold, and/or molten material. In addition to the sensor dataobtained from the one or more sensors, the controller can analyze themodel of the injection molding machine when automatically determiningthe tuning adjustments to one or more control parameters.

Analyzing the models of the injection molding machine, mold, and/ormolten material to determine a composite score and/or to adjust thecontrol parameters further reduces the error between the setpointpattern and the exhibited response by tailoring control to the specificoperating equipment. Consequently, the consistency of the molded productis increased, thereby enabling the production of molded products thatcan achieve tighter tolerances than achievable using conventionaltechniques.

BRIEF DESCRIPTION OF THE DRAWINGS

While the specification concludes with claims particularly pointing outand distinctly claiming the subject matter that is regarded as thepresent invention, it is believed that the invention will be more fullyunderstood from the following description taken in conjunction with theaccompanying drawings. Some of the figures may have been simplified bythe omission of selected elements for the purpose of more clearlyshowing other elements. Such omissions of elements in some figures arenot necessarily indicative of the presence or absence of particularelements in any of the exemplary embodiments, except as may beexplicitly delineated in the corresponding written description. None ofthe drawings are necessarily to scale. For example, the dimensionsand/or relative positioning of some of the elements in the figures maybe exaggerated relative to other elements to help to improveunderstanding of various embodiments of the present invention.

FIG. 1A illustrates a schematic view of an example injection moldingmachine having a controller coupled thereto in accordance with variousembodiments of the present disclosure;

FIG. 1B illustrates a schematic view of an example injection moldingmachine having a controller and a PID controller coupled thereto inaccordance with various embodiments of the present disclosure;

FIG. 2A illustrates a comparison plot between setpoint pressure valuesand sensed pressure values for an injection cycle executed by theinjection molding machine constructed according to the disclosure;

FIG. 2B illustrates particular aspects of the comparison plot of FIG.2A;

FIG. 3 illustrates a comparison plot of a operation parameter metric andan injection cycle index of a run of injection cycles; and

FIG. 4 illustrates an exemplary method for autotuning control parametersof an injection molding process.

DETAILED DESCRIPTION

Referring to the figures in detail, FIG. 1A illustrates an exemplaryinjection molding machine 10 for producing thermoplastic parts in highvolumes (e.g., a class 101 or 30 injection mold, or an “ultra-highproductivity mold”), especially, but not necessarily, thinwalled partshaving an L/T ratio of 100 or greater. The injection molding machine 10generally includes an injection system 12 and a clamping system 14. Athermoplastic material may be introduced to the injection system 12 inthe form of thermoplastic pellets 16. The thermoplastic pellets 16 maybe placed into a hopper 18, which feeds the thermoplastic pellets 16into a heated barrel 20 of the injection system 12. The thermoplasticpellets 16, after being fed into the heated barrel 20, may be driven tothe end of the heated barrel 20 by a ram, such as a reciprocating screw22. The heating of the heated barrel 20 and the compression of thethermoplastic pellets 16 by the reciprocating screw 22 causes thethermoplastic pellets 16 to melt, forming a molten thermoplasticmaterial 24. The molten thermoplastic material is typically processed ata temperature of about 130° C. to about 410° C.

The reciprocating screw 22 forces the molten thermoplastic material 24toward a nozzle 26 to form a shot of thermoplastic material, which willbe injected into a mold cavity 32 of a mold 28 via one or more gates.The molten thermoplastic material 24 may be injected through a gate 30,which directs the flow of the molten thermoplastic material 24 to themold cavity 32. In other embodiments the nozzle 26 may be separated fromone or more gates 30 by a feed system (not shown). The mold cavity 32 isformed between first and second mold sides 25, 27 of the mold 28 and thefirst and second mold sides 25, 27 are held together under pressure by apress or clamping unit 34. The press or clamping unit 34 applies aclamping force during the molding process that is greater than the forceexerted by the injection pressure acting to separate the two mold halves25, 27, thereby holding the first and second mold sides 25, 27 togetherwhile the molten thermoplastic material 24 is injected into the moldcavity 32. In a typical high variable pressure injection moldingmachine, the press typically exerts 30,000 psi or more because theclamping force is directly related to injection pressure. To supportthese clamping forces, the clamping system 14 may include a mold frameand a mold base.

Once the shot of molten thermoplastic material 24 is injected into themold cavity 32, the reciprocating screw 22 stops traveling forward. Themolten thermoplastic material 24 takes the form of the mold cavity 32and the molten thermoplastic material 24 cools inside the mold 28 untilthe thermoplastic material 24 solidifies. Once the thermoplasticmaterial 24 has solidified, the press 34 releases the first and secondmold sides 25, 27, the first and second mold sides 25, 27 are separatedfrom one another, and the finished part may be ejected from the mold 28.The mold 28 may include a plurality of mold cavities 32 to increaseoverall production rates. The shapes of the cavities of the plurality ofmold cavities may be identical, similar or different from each other.(The latter may be considered a family of mold cavities).

A controller 50 is communicatively connected to the injection moldingmachine 10 and is configured to execute a set of computer-readableinstructions stored in a non-transitory memory to cause the injectionmolding machine 10 to execute injection cycles (i.e., theabove-described injection molding process). To execute an injectioncycle, the controller 50 may implement an injection pattern thatincludes one or more setpoint values for the control parameters thatform an injection pattern. In some embodiments, the injection patterndefines a substantially constant pressure profile. Of course, theinjection pattern may define other pressure profiles (e.g., aconventional, high pressure injection molding process).

The controller 50 is also communicatively coupled to one or more sensors52, such as the illustrated nozzle sensor, to measure operation of theinjection molding machine 10. Although FIG. 1A only depicts a nozzlesensor and a screw position sensor, it should be appreciated that thecontroller 50 may monitor the data generated by any number of sensors52. In various embodiments, the sensors 52 may include any number oftemperature sensors, pressure sensors, velocity sensors, and/or positionsensors configured to monitor operation of the injection molding machine10. Additionally, the sensors 52 may include sensors that monitor theenvironment surrounding the injection molding machine 10. For example,the sensors 52 may include a humidity sensor, a temperature sensor, analtitude sensor, a barometer, and/or a seismometer.

According to disclosed embodiments, the controller 50 is alsooperatively connected to a model database 66 that stores modelsindicative of characteristics of the injection molding machine 10, themold 28, and/or the molten thermoplastic material 24 (or, in someembodiments, the thermoplastic pellets 18 in the hopper 16). Forexample, the model of the injection molding machine 10 may indicate aresistivity of one or more components of the injection molding machine10, a number of injection cycles executed using the injection moldingmachine 10, a known error for one or more process variables introducedby the injection molding machine 10, a purge pot pressure of theinjection molding machine 10, and/or a dead head pressure of theinjection molding machine 10. As another example, the model of the mold28 may indicate a resistivity of the mold walls of the mold 28, a numberof injection cycles executed using the mold 28, and/or a material fromwhich the mold 28 is made. As still another example, the model of themolten thermoplastic material 24 may indicate a MFI and/or factorindicative of how MFI changes based on the amount of regrind introducedinto the hopper 18. Although FIG. 1A depicts the model database 66 as asingle entity, in some embodiments, the model database 66 may be dividedinto or made redundant using any number of database entities. The datapopulating the model database 66 may be stored on a non-transitorycomputer readable data storage medium, such as a read/write data storagemedium that is associated with one or more components of the injectionmolding machine 10, the mold 28, and/or a storage container or bagcontaining thermoplastic pellets 18 of the thermoplastic material 24.

Prior to executing a run of injection cycles, the controller 50 mayobtain and analyze the model for the injection molding machine 10, themold 28, and/or the molten thermoplastic material 24 to set an initialvalue for one or more control parameters of the injection moldingmachine. For example, the control parameters may be associated withcomponent setpoint patterns that define a series of setpoint values fora particular control parameter over the course an injection cycle (suchas melt pressure, injection velocity, hold pressure exerted by theclamping unit 34, and/or position of the screw 22). The controlparameters may also include parameters that are substantially constantthroughout the injection cycle (such as temperature of the heated barrel20). Additionally or alternatively, the controller 50 may analyze anyenvironmental sensors 52 to set the initial values for the one or morecontrol parameters.

In some embodiments, the controller 50 determines the initial values byinputting the model data and/or the sensor data into a machine learningmodel. In these embodiments, the machine learning model may be trainedon historical data of prior injection cycles executed using the same orother injection molding machines, molds, and/or material. Based on thetrained relationships between the model data and/or the sensor data, themachine learning model may generate a set of initial values thatminimizes the error between the expected operation of the injectionmolding machine 10 and the injection pattern indicated by the injectioncycle and/or produces more consistent molded parts.

In the embodiment illustrated in FIG. 1B, the controller 50 is alsooperatively connected to a proportional-integral-derivative (PID)controller 60. In these embodiments, the PID controller 60 is configuredto control a particular control parameter of the injection moldingprocess. In operation, the PID controller 60 compares one or moresetpoint values 58 (such as the target setpoint values included in aninjection pattern) for the control parameter to the measured value ofthe control parameter via an adder or comparator 55. For example, thePID controller 60 may be configured to control injection pressure or aninjection velocity. Accordingly, one of the sensors 52 may be configuredto monitor the injection pressure or injection velocity. In someembodiments, the sensor data is communicated directly to the comparator55. In other embodiments, the sensor data is communicated to thecontroller 50 and/or the PID controller 60 which routes the sensor datato the comparator 55.

After the controller 50 determines the initial values of the controlparameters for the injection molding process, the controller 50 executesa run of injection cycles (i.e., a series of sequentially executedinjection cycles using the injection molding machine 10). As describedherein, over the course of the run, operation of the injection moldingmachine 10 shifts. For example, the viscosity of the molten material 24may shift, the temperature of the environment may shift, or traceamounts of the molten material 24 may be deposited on the mold 28. As aresult, the initial values may no longer be optimal for operating theinjection molding machine 10 via the initial injection pattern.Accordingly, after each injection cycle of the run, the controller 50may be configured to analyze the operational parameters of the priorinjection cycle to automatically determine whether or not the controlparameters for the injection molding process should be adjusted (e.g.,“auto-tuned”).

With reference to FIG. 2A, illustrated is a comparison plot betweensetpoint melt pressure values 102 (e.g., melt pressure controlparameters) and the sensed melt pressure values 104 (e.g., measuredoperational parameters) for an injection cycle executed by the injectionmolding machine 10. It should be appreciated that while the illustratedplot is based on a substantially constant pressure profile, thedisclosed techniques may be applied to any suitable pressure profile. Inembodiments that implement the substantially constant pressure profile,the sensed melt pressure values 104 may be generated by a nozzle sensorof the sensors 52 and communicated to the controller 50 during theexecution of the injection cycle. During an initial phase of theinjection cycle, pressure rapidly increases to a setpoint value(setpoint P_(fill)). In the fill phase, the pressure is held at thesteady-state pressure value as the mold cavity 32 is filled. When moltenmaterial 24 nears the end of the mold cavity 32, pressure is reduced tosecond, lower, setpoint value (setpoint P_(Hold)). In the pack and holdphase, the pressure is held at the steady-state pressure value as themolten material 24 in the mold cavity 32 cools. After the material 24 iscooled, the mold 28 is opened in the molded part is ejected from themold cavity 32.

However, as illustrated, the sensed melt pressure values 104 do notmatch the setpoint pressure values 102. Accordingly, in someembodiments, the controller 50 is configured to analyze thesedifferences to determine the need to adjust the control parameters. Forexample, the controller 50 may determine a metric indicative of thedifference between the setpoint P_(fill) and the measured P_(Fill) orthe difference between the setpoint P_(Hold) and the measured P_(Hold).As another example, the controller 50 may determine a metric indicativeof the total amount of error 103 between the setpoint pressure values102 and the sensed pressure values 104.

According to aspects of this disclosure, when the injection moldingmachine 10 exhibits a step response (such as the one indicated by thesetpoint values 102), the sensed pressure values 104 do not immediatelyreach the steady-state value 102. Instead, as illustrated in FIG. 2B,the response overshoots the steady-state value 102 and exhibitsdecreasing oscillatory error until achieving the steady-state value.Accordingly, while FIG. 2A illustrates the sensed pressure curve 104without the overshoot, the sensed pressure curve may actually exhibitthe oscillatory error indicated by the pressure curve 105 as shown inFIG. 2B. The difference between the overshoot pressure associated withthe step response of the pressure curve 105 and the setpoint 102 isreferred to as the “P_(Overshoot).” Similarly, when the controller 50compensates for the overshoot pressure, the pressure curve 105 exceedsthe setpoint value 102 again. The difference between the amount thepressure curve 105 exceeds the setpoint 102 is referred to as the“P_(Undershoot).” Accordingly, in some embodiments, the controller maybe configured to determine a metric based on the P_(Overshoot) orP_(Undershoot) values to determine the need to adjust the controlparameters.

It should be appreciated that FIGS. 2A and 2B only illustrate someexample operational parameters that may be analyzed by the controller 50to determine the need to adjust the control parameters. In variousembodiments, the controller 50 may analyze other operational parameters(such as injection velocity, screw position, clamping pressure, etc.) todetermine the need to adjust the control parameters.

Regardless of the particular operational parameter, the controller 50may compare the value for the operational parameter to a threshold todetermine the need to adjust the control parameters. Referring to FIG.3, illustrated is a comparison plot of an operational parameter metricand an injection cycle index of a run of injection cycles. Theoperational parameter metric values 114 (illustrated as “X”s) representthe value of the operational parameter during each injection cycle ofthe run. The controller 50 has defined an expected range of operationthat includes an upper bound threshold 112 a and a lower bound threshold112 b. Accordingly, the controller 50 may detect when the value of themetric exceeds the threshold 112 a (as illustrated by the value 114 b).In response, the controller 50 autotunes the control parameters. As aresult, as illustrated, the next value 114 is within the thresholds 112a and 112 b.

It should be appreciated that term “exceeds a threshold” does notnecessarily refer to the operational parameter exceeding an upper boundof expected operation, such as the threshold 112 a. In other scenarios,the controller 50 may determine the need to adjust the controlparameters based on the metric exceeding the lower bound threshold 112b.

FIG. 4 illustrates an exemplary method 200 for autotuning controlparameters of an injection molding process. The method 200 may beperformed by a controller 50 operatively connected to the injectionmolding machine 10 of FIG. 1A or 1B.

The example method 200 begins by the controller 50 analyzing a model ofat least one of the injection molding 10, the mold 28, and a moltenmaterial 24 to determine initial values for one or more controlparameters of the injection molding machine 10 (block 202). As describedabove, the controller 50 may obtain the models from the model database66. In addition to any data included in the models, the controller 50may analyze data generated by the sensors 52, including sensorsconfigured to sense environmental conditions associated with theinjection molding machine 10. In some embodiments, the controller 50utilizes the model data (and any sensor data) as an input into a machinelearning algorithm that generates the initial values for the one or morecontrol parameters.

At block 204, the controller 50 executes a run of injection cycles atthe injection molding machine 10. During each injection cycle of therun, the injection molding machine 10 injects the molten material 24into a cavity 32 of the mold 28 according to an injection pattern. Theinjection pattern may define one or more setpoint patterns for one ormore control parameters. For example, the injection pattern may define asetpoint pattern for melt pressure, screw position, screw velocity, holdor clamp pressure, and so on.

At block 206, the controller 50 measures operation of the injectionmolding machine 10 during a particular injection cycle of the run ofinjection cycles. In some embodiments, the controller 50 measuresoperation of the injection molding machine 50 after the controller 50finishes controlling the injection molding machine 10 to execute theparticular injection cycle. To measure the operation of the injectionmolding machine 10, the controller 50 may obtain data sensed by thesensors 52 configured to monitor various conditions of the injectionmolding process.

At block 208, the controller 50 determines that one or more operationalparameters exceeds a threshold. The operational parameters may include asteady-state error, an overshoot pressure, an undershoot pressure, anenvironmental parameter, and so on. Accordingly, the controller 50 maycompare a value for a particular operational parameter to the threshold.In some embodiments, the threshold may be indicative of a viscosity ofthe molten material 24 and/or a molded part weight (which can be used asan indication of part-to-part consistency) being outside of an expectedrange of operation.

Additionally or alternatively, the controller 50 may combine two or moreof the operational parameters to generate a composite metric. In someembodiments, the controller 50 assigns the individual operationalparameters a weight or weighting function to combine the operationalparameters into the composite metric. For example, the weights orweighting functions may be indicative of the amount the particularoperational parameter impacts the viscosity of the molten material 24and/or the molded part weight. Accordingly, in these embodiments, thecontroller 50 compares the composite metric to the threshold.

In some embodiments, the controller 50 applies a machine learningalgorithm to determine the composite metric. More particularly, thecontroller 50 may apply machine learning techniques to determine theweights and/or weighting functions for the operational parameterscombined into the composite metric. In some embodiments, the machinelearning model that determines the weights used to develop the compositemetric may be a different machine learning model than the model used todetermine the initial control values. In these embodiments, while bothmachine learning models may be trained based on data collected duringprior injection cycles executed using the same or different injectionmolding machines, molds, and/or molten materials, the machine learningmodel that determines the weights associated with the operationalparameters may be configured to determine a need to autotune the controlparameters, but not necessarily the particular values to which thecontrol parameters are tuned. In other embodiments, the same machinelearning model determines both the weights or weighting function tocombine the operational parameters to generate the composite metric, aswell as the values to which the control parameters are tuned.

At block 210, upon determining that the one or more operationalparameters exceeds the threshold, the controller 50 adjusts the controlparameters for subsequent injection cycles of the run of injectioncycles. In some embodiments, the controller 50 adjusts one or moresetpoint patterns for the control parameters that form the injectionpattern. In embodiments that include the PID controller 60 beingoperatively connected to the injection molding machine 10 as illustratedin FIG. 1B, the controller 50 may adjust one or more of the gains of thePID controller 60. To this end, the PID controller 60 may include one ormore interfaces to receive commands to configure the proportional,integral, and/or derivative gains. The interfaces may includeapplication-layer interfaces, such as an application programminginterface (API), and a communication interface, such as a wired orwireless communication link. The controller 50 may generate a command toadjust the proportional, integral, and/or derivative gains in a formatdefined by the API of the PID controller 60 and transmit the command tothe PID controller 60 via the wired or wireless communication link.

In some embodiments, the controller 50 applies a machine learningalgorithm to determine an adjustment to the control parameters. Forexample, the controller 50 may utilize the machine learning algorithmused to generate the initial values for the control parameters todetermine the adjustment. As described above, the environment and/or theoperation of the injection molding machine 10 changes throughout thecourse of a run. Accordingly, when the controller 50 utilizes theupdated set of operational data as an input, the machine learningalgorithm may produce a different set of control parameter values. Thecontroller 50 may analyze this output set of control parameters valuesto determine the adjustment to the one or more control parameters. As aresult, when the controller 50 controls the injection molding machine 10to execute subsequent injection cycles, the consistency in molded partsis improved.

It should be appreciated that a run may include a sufficient number ofinjection cycles that the operational parameters may continue to shift,thereby causing the operation of the injection molding machine 10 to beoutside of the expected range of operation. Accordingly, the controller50 may be configured to execute the actions associated with blocks206-210 after each subsequent injection cycle of the run.

It should be understood that the term “control parameter” generallyrefers to an input into the injection molding process controlled by acontroller and the term “operational parameter” generally refers tomeasured characteristic of the injection molding process duringoperation. In some embodiments, the same characteristic of the injectionmolding process may be both a control parameter and an operationalparameter. For example, a melt pressure may be associated with a controlparameter (e.g., a setpoint value or injection pattern) and anoperational parameter (e.g., a sensed pressure value via a physical orvirtual sensor).

Those skilled in the art will recognize that a wide variety ofmodifications, alterations, and combinations can be made with respect tothe above described embodiments without departing from the scope of theinvention, and that such modifications, alterations, and combinationsare to be viewed as being within the ambit of the inventive concept.

The patent claims at the end of this patent application are not intendedto be construed under 35 U.S.C. § 112(f) unless traditionalmeans-plus-function language is expressly recited, such as “means for”or “step for” language being explicitly recited in the claim(s). Thesystems and methods described herein are directed to an improvement tocomputer functionality, and improve the functioning of conventionalcomputers.

What is claimed is:
 1. An injection molding system comprising: aninjection molding machine; a mold; a model database configured to storemodels for (i) the injection molding machine and (ii) the mold; and acontroller operatively connected to the model database and to theinjection molding machine, the controller configured to: analyze themodel of at least the injection molding machine, wherein the model ofthe injection molding machine includes an indication of a resistivityfor one or more parts of the injection molding machine; determineinitial values for one or more control parameters of the injectionmolding machine based at least upon the resistivity for the one or partsof the injection molding machine; execute a run of injection cycles atthe injection molding machine, wherein during each injection cycle ofthe run, the injection molding machine injects a molten material into acavity of the mold according to an injection pattern; measure operationof the injection molding machine during a particular injection cycle ofthe run of injection cycles; determine one or more operationalparameters exceed a threshold; and upon determining that the one or moreoperational parameters exceed the threshold, adjust the one or morecontrol parameters for subsequent injection cycles of the run ofinjection cycles.
 2. The system of claim 1, further comprising: aproportional-integral-derivative (PID) controller configured to controla control parameter of the one or more control parameters, the PIDcontroller having (i) a first gain associated with a proportionalcomponent; (ii) a second gain associated with an integral component; and(iii) a third gain associated with a derivative component.
 3. The systemof claim 2, wherein to adjust the one or more control parameters, thecontroller is configured to: adjust one of the first, second, or thirdgains of the PID controller.
 4. The system of claim 1, wherein theinjection pattern defines one or more setpoint patterns for the one ormore control parameters.
 5. The system of claim 4, wherein to adjust theone or more control parameters, the controller is configured to: adjusta setpoint pattern for the one or more setpoint patterns.
 6. The systemof claim 1, wherein the model database includes a model of the moltenmaterial.
 7. The system of claim 6, wherein to determine the initialvalues for the one or more control parameters, the controller isconfigured to: analyze the model of the molten material.
 8. The systemof claim 1, wherein the one or more operational parameters include twoor more operational parameters, and wherein to determine the one or moreoperational parameters exceed the threshold, the controller isconfigured to: combine a value for two or more of the operationalparameters to generate a composite metric.
 9. An injection moldingsystem comprising: an injection molding machine; a mold; a modeldatabase configured to store models for (i) the injection moldingmachine and (ii) the mold; and a controller operatively connected to themodel database and to the injection molding machine, the controllerconfigured to: analyze the model of at least the mold, wherein the modelof the mold includes data associated with historic injection cyclesexecuted using the mold; determine initial values for one or morecontrol parameters of the injection molding machine based at least uponthe mold data associated with the historic injection cycles; execute arun of injection cycles at the injection molding machine, wherein duringeach injection cycle of the run, the injection molding machine injects amolten material into a cavity of the mold according to an injectionpattern; measure operation of the injection molding machine during aparticular injection cycle of the run of injection cycles; determine oneor more operational parameters exceed a threshold; and upon determiningthat the one or more operational parameters exceed the threshold, adjustthe one or more control parameters for subsequent injection cycles ofthe run of injection cycles.
 10. The system of claim 9, furthercomprising: a proportional-integral-derivative (PID) controllerconfigured to control a control parameter of the one or more controlparameters, the PID controller having (i) a first gain associated with aproportional component; (ii) a second gain associated with an integralcomponent; and (iii) a third gain associated with a derivativecomponent.
 11. The system of claim 10, wherein to adjust the one or morecontrol parameters, the controller is configured to: adjust one of thefirst, second, or third gains of the PID controller.
 12. The system ofclaim 9, wherein the injection pattern defines one or more setpointpatterns for the one or more control parameters.
 13. The system of claim12, wherein to adjust the one or more control parameters, the controlleris configured to: adjust a setpoint pattern for the one or more setpointpatterns.
 14. The system of claim 9, wherein the model database includesa model of the molten material.
 15. The system of claim 14, wherein todetermine the initial values for the one or more control parameters, thecontroller is configured to: analyze the model of the molten material.16. The system of claim 9, wherein the one or more operationalparameters include two or more operational parameters, and wherein todetermine the one or more operational parameters exceed the threshold,the controller is configured to: combine a value for two or more of theoperational parameters to generate a composite metric.
 17. A method forcontrolling an injection molding machine and a mold forming a moldcavity, the injection molding machine being controlled according to aninjection cycle, the method comprising: analyzing a model of at leastthe injection molding machine, wherein the model of the injectionmolding machine includes an indication of a resistivity for one or moreparts of the injection molding machine; determining initial values forone or more control parameters of the injection molding machine based atleast upon the resistivity for the one or parts of the injection moldingmachine and the mold data associated with historic injection cycles;executing a run of injection cycles at the injection molding machine,wherein during each injection cycle of the run, the injection moldingmachine injects a molten material into the mold cavity according to aninjection pattern; measuring operation of the injection molding machineduring a particular injection cycle of the run of injection cycles;determining one or more operational parameters exceed a threshold; andupon determining that the one or more operational parameters exceed thethreshold, adjusting the one or more control parameters for subsequentinjection cycles of the run of injection cycles.
 18. The method of claim17, wherein the one or more operational parameters include one or moreof a steady-state error, an overshoot pressure, an undershoot pressure,and an environmental parameter.
 19. The method of claim 17, wherein: theinjection pattern defines one or more setpoint patterns for the one ormore control parameters; and adjusting the one or more controlparameters includes adjusting the one or more setpoint patterns includedin the injection pattern.
 20. A method for controlling an injectionmolding machine and a mold forming a mold cavity, the injection moldingmachine being controlled according to an injection cycle, the methodcomprising: analyzing a model of at least the mold, wherein the model ofthe mold includes data associated with historic injection cyclesexecuted using the mold; determining initial values for one or morecontrol parameters of the injection molding machine based at least upont the mold data associated with the historic injection cycles; executinga run of injection cycles at the injection molding machine, whereinduring each injection cycle of the run, the injection molding machineinjects a molten material into the mold cavity according to an injectionpattern; measuring operation of the injection molding machine during aparticular injection cycle of the run of injection cycles; determiningone or more operational parameters exceed a threshold; and upondetermining that the one or more operational parameters exceed thethreshold, adjusting the one or more control parameters for subsequentinjection cycles of the run of injection cycles.